Massive multiple-input multiple-output (MIMO) systems are a core technology designed to achieve the performance objectives defined for 5G wireless communications. They achieve high spectral efficiency, reliability, and diversity gain. However, the many radio frequency chains required in base stations equipped with a high number of transmit antennas imply high hardware costs and computational complexity. Therefore, in this paper, we investigate the use of a transmit-antenna selection scheme, with which the number of required radio frequency chains in the base station can be reduced. This paper proposes two efficient transmit-antenna selection (TAS) schemes designed to consider a trade-off between performance and computational complexity in massive MIMO systems. The spectral efficiency and computational complexity of the proposed schemes are analyzed and compared with existing TAS schemes, showing that the proposed algorithms increase the TAS performance and can be used in practical systems. Additionally, the obtained results enable a better understanding of how TAS affects massive MIMO systems.

Underground utility tunnels (UUTs) are convenient for the integrated management of various infrastructure facilities. They ensure effective control of underground facilities and reduce occupied space. However, aging UUTs require effective management and preventive measures for fire safety. The fundamental problems in operating UUTs are the frequent occurrence of mold, corrosion, and damage caused to finishing materials owing to inadequate waterproofing, dehumidification, and ventilation facilities, which result in corrosion-related electrical leakage in wiring and cables. To prevent this, an abnormal sound detection technology is developed in this study based on acoustic sensing. An acoustic sensor is used to detect electric sparks in the moldy environments of UUTs using a system to collect and analyze the sound generated in the UUTs. We targeted the sound that had the highest impact on detecting electric sparks and performed U-Net-based noise reduction and two-dimensional convolutional neural network-based abnormal sound detection. A mock experiment was conducted to verify the performance of the proposed model. The results indicated that local and spatial features could capture the internal characteristics of both abnormal and normal sounds. The superior performance of the proposed model verified that the local and spatial features of electric sparks are crucial for detecting abnormal sounds.

Urban pluvial flooding driven by localized extreme rainfall increasingly exceeds the capacity of metropolitan drainage systems. Manhole surcharge overflow interacting with surface runoff produces complex inundation dynamics that are difficult to capture in real time. High-fidelity 1D–2D coupled numerical models are too computationally expensive for operational deployment, whereas purely data-driven deep-learning surrogates, although fast, do not enforce conservation laws or provide physically interpretable behaviour. We propose a modular coupled physics-informed neural network (MC-PINN) that bridges this gap. MC-PINN consists of a 1D PINN for sewer network flow governed by the Saint-Venant equations and a 2D PINN for surface flow governed by shallow-water equations, coupled through manhole overflow acting as a boundary condition. Mass continuity is enforced at the interface and momentum is strongly constrained via physics-based residual losses, enabling a hybrid surrogate with near real-time inference potential. To demonstrate feasibility, we present a manufactured-solution proof-of-concept in which a 1D advection PINN and a 2D diffusion PINN are coupled via a shared interface signal. The example shows that MC-PINN can jointly approximate both PDE fields and a consistent interface, supporting the mathematical viability of the modular coupling.